Cade Metz - Genius Makers

 

Lines I underline from the book:

[Geoffrey Hinton] was the great-great-grandson of both George Boole, the nineteenth-century British mathematician and philosopher whose "Boolean logic" would provide the mathematicak foundation for every modern computer, and ...

When asked about Rumelhart, Hinton liked to recall the time they were stuck listening to a lecture that held absolutely no interest for either of them. When the lecture ended, as Hinton complained that he had just lost an hour of his life, Rumelhart said he didn't really mind. If he just ignored the lecture, Rumelhart said, he had sixty uninterrepted minutes to think about his own research. For Hinton, this epitomized his long-time collaborator.

Hinton liked to say that "old ideas are new" - that scientists should never give up on an idea unless someone had proven it wouldn't work.

When anyone asked Feynman to explain the work that won him the Nobel Prize in terms the layperson could understand, he, too, would decline. "If I could explain it to the average person," he would say, "it wouldn't have been worth the Nobel Prize."

"[Demis Hassabis]'s got three things," Hinton says. "He's very bright, he's very competitive, and he's very good at social interactions. That's a dangerous combination."

LeCun believed in "openness" - concepts, algorithms, and techniques openly shared with the wider community of researchers, not sequestered inside a single company or university. The idea was that this free exhange of information accelerated the progress of research as a whole.

[Nick Bostrom] 's concern was. that scientists would design as system to perfect a particular part of our lives without realizing it would one day wreak havoc in ways no one had the power to stop.

Some researchers argued that new technologies could be built to solve this issue [black box]. But it was a far from trivial problem. "Don't believe anyone who says that it is," Geoff Hinton  told the New Yorker in a sweeping feature story on the rise of deep learning in healthcare... and that the black-box problem was something people would learn to live with. The trick was convincing the world it was not a problem, and this would come through testing - proof that even if you could not see inside of them, they did what they were supposeed to do.

[Qi Lu]'s argument wasn't that Microsoft should sell a driverless car. I was that Microsoft should build one. This would give the company the skills and the technologies and the insight it needed to succeed in so many other areas... "It is not that people at Google drink different waters," he said. "The search engine required them to solve a set of technological challenges." Building a self-driving car, Lu believed, would enrich Microsoft's future in the same way. "We must put ourselves in a position to see the future of computing."

[Yoshua Bengio] said that the robots of the future would need to sleep. They would need to sleep, he argued, because they would need to dream. His point was that the future of AI research lay with systems that could not only recognize pictures and spoken words but also generate them on their own. Dreaming is a vital part of the way humans learn.

In each socioeconomic era, [Lu] liked to say,  there was one primary means of production. In the agricultural era, it was about the land. "It doesn't matter how many people you have. I doesn't matter how brilliant you are. You cannot produce more if you do not have more land." In the industrial era, it was about labor and equipment. In the new era, it was about data. "Without data, you cannot build a speech recognizer. It does not matter how many people you have. You may have one million brilliant engineers, but you won't be able to build a system that understands language and can have a dialogue. You won't be able to build a system that can recognize images like I do right now."

"Excessive automation at Tesla was a mistake," [Elon Musk] said. "Humans are underrated."

Photo: https://www.amazon.com.tr/Genius-Makers-Mavericks-I-Facebook/dp/1847942148